OPTIMIZING HELICOPTER TRANSPORT OF OIL RIG CREWS AT PETROBRAS

1. OPTIMIZING HELICOPTER TRANSPORT OF OIL RIG CREWS AT PETROBRAS Ayberk Göksenin ÜLKER Samet AKÇA Feyza KESKİN

2. OUTLINE • Introduction • Current Process • Problem Definition • Related Work • Project Description • Model • Solution Methodology • Evaluation and Benefits • Additional Examples and Comparison • Questioning and Conclusion • References

3. INTRODUCTION • Petrobras • Founded in 1953 • Major oil producer of Brazil • Under management of government • 25,000 workers • Oil production of Brazil: 2 million barrels/day, 13th in world • 90% by Petrobras • Milestone: 1974 - Campos basin explored

4. CURRENT PROCESS • 80 offshore oil-production platforms • 1,900 workers to be transported by helicopter • Between platforms and 4 mainland bases • 2-weeks shift, 3-weeks rest • Largest non-military helicopter operations

5. CURRENT PROCESS • Macaé: • 65 daily flights • 33 helicopters • São Tomé: • 30 daily flights • 7 helicopters • Jacarepaguá & Vitória • 15 daily flights • 5 helicopters

6. CURRENT PROCESS • Flight and passenger assignments done manually, based on • Travel demands • Departure time and destination • Selected from a fixed timetable by passengers • Helicopter availability

7. PROBLEM DEFINITION • Complexities • Limited number of available helicopters • Strict operational rules • 8 types of helicopter with different • Operational characteristics • Capacity • Cost

8. PROBLEM DEFINITIONObjectives • Output required each day at each airport including • Flight scheduling • Helicopter routing • Assignment of workers to flights • Output required within 1 hour

9. PROBLEM DEFINITIONObjectives • Satisfy all demands • Improve safety • Reduce number of landings • Minimize costs • Helps decreasing flight time

10. PROBLEM DEFINITIONConstraints • Flights start and finish at same base • Max 5 fligths/day for each helicopter • Inspection time between flights • Limited number of landings for each flight • Limited number of legs for each passenger • Limited number of helicopters visiting same platform for each departure time • Lunch stops • Helicopter capacity (determined by route length)

11. RELATED WORKin Petrobras • Investments in IT to assist manual operation • Attempts to implement a decision support system, by Galvão & Guimarães (1990) • Routes for fixed departure times • Unsuccessful due to worker resistance • Not fully automated, still required manual input

12. RELATED WORKin Literature • Helicopter-scheduling studies • Timlin & Pulleyblank (1992) • Heuristics, not concerned with time factor • Tjissen (2000) • SDVRP, constant capacity • Hernadvolgyi (2004) • Single helicopter

13. PROJECT DESCRIPTION • Contract signed with Gapso • Operational version of scheduling system (2005) – 50 weeks • IT functionality (2006) – 6 months • MPROG • 2005 – São Tomé • 2006 – Macaé • 2008 – Vitória & Jacarepaguá • 5 years contract for support and improvement • Training and assistance

14. MODEL • Billions of variables • NP-hard • Generalization of SDVRP

15. Solution Method • Column-generation • Network flow formulation  assign passengers to previously selected routes, employs heuristics • Which variables to use for a good solution • Challenge: maximum possible number of passengers being picked for each demand  adhf= qd or remaining capacity  required columns cannot be generated • Solution: Disaggregating demands  adhf = 1 if corresponding passenger is on the flight

16. Solution Method • Column-generation sub-problem • Dual variables: , • Computation of reduced cost of :  Determine h and f with minimum and satisfy landing number constratins  NP-hard (prize collecting TSP)

17. Solution Method • Heuristic Procedure • Most departure times in timetable serve small number of platforms • Max 5 landings in each flight • For each departure time and helicopter, seeking profitable flights, with fixed number of landings

18. Solution Method • Heuristic Procedure • Generate all possible routes with 1 or 2 landings • Generate routes with 3,4 and 5 landings by neighborhood search • For each route, compute • Solve minimum-cost-flow problem to assign passengers • Sum and (optimal value of MCF) to find • Incorporate with negative ’s into the restricted integer program • Stop local search when a column with negative reduced cost is found

19. Solution Method • MCF network: • Stop nodes: bases & platforms • Demand nodes: passengers • Optimum flow value:  = (computed before)

20. Solution Method • Main Algorithm • Decompose the problem: Generation of flights & assembly • Assembly done by integer programming model

21. Solution Method • To ease the solution of MIP constraints are relaxed • Equations to ≥ inequalities • Allowing demand to be oversatisfied • Postoptimization:

22. Evaluation and Benefits • 18% fewer landings • 8% less flight time • 14% reduction in costs • Annual saving: ~ $24 million • Scheduling process improved • In afternoon, schedules of next day can be generated • Time for analysis and adjustments if necessary • Human factor eliminated

23. Evaluation and Benefits • Before (manual method observation for 354 days): • On 255 days: landings on same platform limit violated • On 202 days: inspection between flights violated • On 212 days: capacity was exceeded • In Macaé savings of $50,000/day estimated, compared to manual schedules. • Safety level increased

24. Additional Examples and Comparison • Turkey: Hierarchicalanalysis of helicopterlogistics in disasterreliefoperationsby Gülay Barbarosoğlu, Linet Özdamarand Ahmet Çevik • Aim: scheduling helicopter activities in a relief disaster operation • Assigning, scheduling and routing of pilots, flights and helicopters • Mixed Integer Programming was developed with makespan minimization objective

25. Additional Examples and Comparison • Abroad: Routing helicopters for crew exchanges on off-shore locations (North Sea-Holland) • Aim: determining a flight schedule for helicopters andexchanging crew withminimizing the costof flights. • Determined as Split Delivery Vehicle Routing Problem(SDVRP) • Column generation procedure was used

26. Conclusion • MPROG is used at Campos basin rigs • Planned integration with flight and passenger control systems • 5 years contract, still in use • Dynamic development and changes required due to variabilities of recent reserve discoveries • 2009 finalist in the Wagner Prize, an INFORMS award for the best cases of practical use of Operational Research

27. References • http://www.gapso.com.br/en/the-gapso-solution-could-save-us-24-million-per-year-in-aircraft-operations/ • http://www.petrobras.com/en/about-us/